code: def to_wider_graph(graph):
      """wider graph"""
      weighted_layer_ids = graph.wide_layer_ids()
      weighted_layer_ids = list(
          filter(
              lambda x: graph.layer_list[x].output.shape[-1],
              weighted_layer_ids,
          )
      )
      wider_layers = sample(weighted_layer_ids, 1)
  
      for layer_id in wider_layers:
          layer = graph.layer_list[layer_id]
          if is_layer(layer, "Conv"):
              n_add = layer.filters
          else:
              n_add = layer.units
  
          graph.to_wider_model(layer_id, n_add)
      return graph
  
file_path: nni/nni/algorithms/hpo/networkmorphism_tuner/graph_transformer.py
imports: from copy import deepcopy
  from random import randrange, sample
  from .graph import NetworkDescriptor
  from .layers import StubDense, StubReLU, get_batch_norm_class, get_conv_class, get_dropout_class, get_pooling_class, is_layer
  from .utils import Constant
label_desc: Use image node_id 1 for calling a global function with example usage: to_wider_graph(graph) and returns: graph